Using piecewise linear (PWL) functions to model discrete data has applications for example
in healthcare, engineering and pattern recognition. Recently, mixed-integer linear …

The well-known spatial sign covariance matrix (SSCM) carries out a radial transform which
moves all data points to a sphere, followed by computing the classical covariance matrix of …

The design of regression models that are not affected by outliers is an important task which
has been subject of numerous papers within the statistics community for the last decades …

In this work we develop two new algorithms for outlier detection in skewed data. The first
algorithm uses an adjusted median with the help of Robust Support Vector Regression and …

Data in practice are often of high dimension and multivariate in nature. Detection of outliers
has been one of the problems in multivariate analysis. Detecting outliers in multivariate data …

Given a dataset an outlier can be defined as an observation that does not follow the
statistical properties of the majority of the data. Computation of the location estimate is of …

The goal of robust statistics is to develop methods that are robust against outliers in the data.
We emphasize on high breakdown estimators, which can deal with heavy contamination in …